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dc.contributor.advisorDonna H. Rhodes.en_US
dc.contributor.authorMahle, Samuel Adam.en_US
dc.contributor.otherMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.contributor.otherTechnology and Policy Program.en_US
dc.date.accessioned2019-09-16T22:35:20Z
dc.date.available2019-09-16T22:35:20Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122189
dc.descriptionThesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 145-151).en_US
dc.description.abstractUncertainty and risk are endemic in research and development (R&D) programs. Managing risks is a pivotal step at all phases of system design, but is particularly important at the beginning phases where system designers attempt to assess many design alternatives before the systems is developed and deployed. Designing systems that mitigate risks or are flexible enough to withstand them can lead to more reliable and valuable systems. If decision-makers believe the risks are too high, they may not invest in a technology that would provide the capabilities they need. Alternatively, if they underestimate the risks of a technology, investment may lead to costly and embarrassing overruns. Unfortunately, the existing approaches that decision-makers use to help them understand their options are often inadequate and do not tell the full story.en_US
dc.description.abstractIn practice, overly simplistic methods can lead to arbitrary analysis, while more complex techniques such as NASA's probabilistic risk assessment procedure can be too costly and time-consuming for the fast-moving R&D context. These shortfalls can lead to results that decision-makers do not trust and ultimately ignore, leading to decisions based on gut-feelings or hunches. In addition to these shortfalls, there exist gaps between the theory upon which many decision methods and their results are based; several researchers have pointed out computational weaknesses in risk matrices, one of the most widely-employed risk assessment tools. Simply put, the decision-making tools that exist today may not be adequate. Moreover, many risk assessments are conducted secondary to initial tradeoff analysis, resulting in suboptimal design decisions that do not account for risks from the beginning.en_US
dc.description.abstractIn R&D programs, decision-makers require a human-centric method of modeling and communicating the benefits, costs, and risks associated with various technologies. This research develops a framework for conducting Visualized Risk-Informed Tradespace Exploration (vRITE) based on insights gained from practicing R&D program managers. vRITE provides decision-makers an interactive, human-centric analysis of the cost, schedule, performance, and risk associated with multiple technologies. Two example vRITE analyses are used illustrate how the framework addresses the real-world objective and subjective considerations that decision-makers care about most and allows them to explore the data, helping them identify which technology warrants investment. The information provided in a vRITE analysis can reduce the barriers to R&D investment and ultimately lead to more risk-informed decision-making.en_US
dc.description.statementofresponsibilityby Samuel Adam Mahle.en_US
dc.format.extent154 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectTechnology and Policy Program.en_US
dc.titleThe development of visualized risk-informed tradespace exploration (vRITE) for R&D investment decision-makingen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Technology and Policyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentTechnology and Policy Programen_US
dc.identifier.oclc1117774805en_US
dc.description.collectionS.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Societyen_US
dspace.imported2019-09-16T22:35:18Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentESDen_US
mit.thesis.departmentIDSSen_US


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